From 6353f3ac1aff1a93d54def083bfa392d7a0f01be Mon Sep 17 00:00:00 2001 From: Stefano Ariestasia Date: Mon, 26 Dec 2022 08:19:51 +0900 Subject: [PATCH] fix formulas and implement new metrics --- freqtrade/data/metrics.py | 28 +++++++++----------------- freqtrade/optimize/optimize_reports.py | 6 ++++-- 2 files changed, 13 insertions(+), 21 deletions(-) diff --git a/freqtrade/data/metrics.py b/freqtrade/data/metrics.py index 00168bbfa..8401e31bb 100644 --- a/freqtrade/data/metrics.py +++ b/freqtrade/data/metrics.py @@ -222,8 +222,8 @@ def calculate_expectancy(trades: pd.DataFrame) -> float: return expectancy -def calculate_sortino(trades: pd.DataFrame, - min_date: datetime, max_date: datetime) -> float: +def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime, + starting_balance: float) -> float: """ Calculate sortino :param trades: DataFrame containing trades (requires columns profit_ratio) @@ -232,18 +232,13 @@ def calculate_sortino(trades: pd.DataFrame, if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date): return 0 - total_profit = trades["profit_ratio"] - days_period = (max_date - min_date).days + total_profit = trades['profit_abs'] / starting_balance + days_period = max(1, (max_date - min_date).days) - if days_period == 0: - return 0 - - # adding slippage of 0.1% per trade - # total_profit = total_profit - 0.0005 expected_returns_mean = total_profit.sum() / days_period trades['downside_returns'] = 0 - trades.loc[total_profit < 0, 'downside_returns'] = trades['profit_ratio'] + trades.loc[total_profit < 0, 'downside_returns'] = (trades['profit_abs'] / starting_balance) down_stdev = np.std(trades['downside_returns']) if down_stdev != 0: @@ -256,8 +251,8 @@ def calculate_sortino(trades: pd.DataFrame, return sortino_ratio -def calculate_sharpe(trades: pd.DataFrame, - min_date: datetime, max_date: datetime) -> float: +def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime, + starting_balance: float) -> float: """ Calculate sharpe :param trades: DataFrame containing trades (requires columns close_date and profit_ratio) @@ -266,14 +261,9 @@ def calculate_sharpe(trades: pd.DataFrame, if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date): return 0 - total_profit = trades["profit_ratio"] - days_period = (max_date - min_date).days + total_profit = trades['profit_abs'] / starting_balance + days_period = max(1, (max_date - min_date).days) - if days_period == 0: - return 0 - - # adding slippage of 0.1% per trade - # total_profit = total_profit - 0.0005 expected_returns_mean = total_profit.sum() / days_period up_stdev = np.std(total_profit) diff --git a/freqtrade/optimize/optimize_reports.py b/freqtrade/optimize/optimize_reports.py index eb635cde6..7de8f1a47 100644 --- a/freqtrade/optimize/optimize_reports.py +++ b/freqtrade/optimize/optimize_reports.py @@ -450,8 +450,8 @@ def generate_strategy_stats(pairlist: List[str], 'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(), 'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']), 'expectancy': calculate_expectancy(results), - 'sortino': calculate_sortino(results, min_date, max_date), - 'sharpe': calculate_sharpe(results, min_date, max_date), + 'sortino': calculate_sortino(results, min_date, max_date, start_balance), + 'sharpe': calculate_sharpe(results, min_date, max_date, start_balance), 'calmar': calculate_calmar(results, min_date, max_date, start_balance), 'profit_factor': profit_factor, 'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT), @@ -795,6 +795,8 @@ def text_table_add_metrics(strat_results: Dict) -> str: ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'), ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor' in strat_results else 'N/A'), + ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy' + in strat_results else 'N/A'), ('Trades per day', strat_results['trades_per_day']), ('Avg. daily profit %', f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),